Knowledge-based computational intelligence development for predicting protein secondary structures from sequences.
In the postgenomic age, with the avalanche of protein sequences generated and relatively slow progress in determining their structures by experiments, it is important to develop automated methods to predict the structure of a protein from its sequence. The membrane proteins are a special group in the protein family that accounts for approximately 30% of all proteins; however, solved membrane protein structures only represent less than 1% of known protein structures to date. Although a great success has been achieved for developing computational intelligence techniques to predict secondary structures in both globular and membrane proteins, there is still much challenging work in this regard. In this review article, we firstly summarize the recent progress of automation methodology development in predicting protein secondary structures, especially in membrane proteins; we will then give some future directions in this research field.
Shen
Hong-Bin
HB
Institute of Image Processing & Pattern Recognition, Shanghai Jiaotong University, 800 Dongchuan Road, Shanghai, 200240, China. hbshen@sjtu.edu.cn
Yi
Dong-Liang
DL
Yao
Li-Xiu
LX
Yang
Jie
J
Chou
Kuo-Chen
KC
D001185:Artificial Intelligence; D008565:Membrane Proteins / Q000737:chemistry; D017433:Protein Structure, Secondary; D020543:Proteome / Q000737:chemistry*; D040901:Proteomics / Q000379:methods*
D008565:Membrane Proteins; D020543:Proteome